Steps

0. Install and load libraries

1. Read in the data

There are 4 datasets to read in, Starbucks locations, Starbucks nutrition, US population by state, and US state abbreviations. All of them are on the course GitHub.

sb_locs <- read_csv("https://raw.githubusercontent.com/JSC370/JSC370-2025/refs/heads/main/data/starbucks/starbucks-locations.csv")

## Rows: 25600 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): Store Number, Store Name, Ownership Type, Street Address, City, St...
## dbl  (2): Longitude, Latitude
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

sb_nutr <- read_csv("https://raw.githubusercontent.com/JSC370/JSC370-2025/refs/heads/main/data/starbucks/starbucks-menu-nutrition.csv")

## Rows: 205 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Item, Category
## dbl (5): Calories, Fat (g), Carb. (g), Fiber (g), Protein (g)
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

usa_pop <- read_csv("https://raw.githubusercontent.com/JSC370/JSC370-2025/refs/heads/main/data/starbucks/us_state_pop.csv")

## Rows: 55 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (1): population
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

usa_states<-read_csv("https://raw.githubusercontent.com/JSC370/JSC370-2025/refs/heads/main/data/starbucks/states.csv")

## Rows: 51 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): State, Abbreviation
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

2. Look at the data

Inspect each dataset to look at variable names and ensure it was imported correctly.

head(sb_locs)

## # A tibble: 6 × 12
##   `Store Number` `Store Name`            `Ownership Type` `Street Address` City 
##   <chr>          <chr>                   <chr>            <chr>            <chr>
## 1 47370-257954   Meritxell, 96           Licensed         Av. Meritxell, … Ando…
## 2 22331-212325   Ajman Drive Thru        Licensed         1 Street 69, Al… Ajman
## 3 47089-256771   Dana Mall               Licensed         Sheikh Khalifa … Ajman
## 4 22126-218024   Twofour 54              Licensed         Al Salam Street  Abu …
## 5 17127-178586   Al Ain Tower            Licensed         Khaldiya Area, … Abu …
## 6 17688-182164   Dalma Mall, Ground Flo… Licensed         Dalma Mall, Mus… Abu …
## # ℹ 7 more variables: `State/Province` <chr>, Country <chr>, Postcode <chr>,
## #   `Phone Number` <chr>, Timezone <chr>, Longitude <dbl>, Latitude <dbl>

head(sb_nutr)

## # A tibble: 6 × 7
##   Item         Category Calories `Fat (g)` `Carb. (g)` `Fiber (g)` `Protein (g)`
##   <chr>        <chr>       <dbl>     <dbl>       <dbl>       <dbl>         <dbl>
## 1 Chonga Bagel Food          300         5          50           3            12
## 2 8-Grain Roll Food          380         6          70           7            10
## 3 Almond Croi… Food          410        22          45           3            10
## 4 Apple Fritt… Food          460        23          56           2             7
## 5 Banana Nut … Food          420        22          52           2             6
## 6 Blueberry M… Food          380        16          53           1             6

head(usa_pop)

## # A tibble: 6 × 2
##   state      population
##   <chr>           <dbl>
## 1 Alabama       4779736
## 2 Alaska         710231
## 3 Arizona       6392017
## 4 Arkansas      2915918
## 5 California   37253956
## 6 Colorado      5029196

head(usa_states)

## # A tibble: 6 × 2
##   State      Abbreviation
##   <chr>      <chr>       
## 1 Alabama    AL          
## 2 Alaska     AK          
## 3 Arizona    AZ          
## 4 Arkansas   AR          
## 5 California CA          
## 6 Colorado   CO

sb_locs = Starbucks locations, including their street address, the city, the state/province, and longitude and latitude
sb_nutr = the nutrient information of the items on the Starbucks menus, including calories, fat, car, fiber, protein
usa_pop = population in the different states in usa
usa_states = abbreviations of all the us states

3. Format and merge the data

Subset Starbucks data to the US.
Create counts of Starbucks stores by state.
Merge population in with the store count by state.
Inspect the range values for each variable.

sb_usa <- sb_locs |> filter(Country == "US")

sb_locs_state <- sb_usa |>
  group_by("State/Province") |>
  rename(state = "State/Province") |>
  group_by(state) |>
  summarize(n_stores = n())

# need state abbreviations
usa_pop_abbr <- 
  full_join(usa_pop, usa_states,
            by = join_by(state == State)
            ) 
  
sb_locs_state <- full_join(sb_locs_state, usa_pop_abbr,
                           by = join_by(state == Abbreviation))
  

summary(sb_locs_state)

##     state              n_stores        state.y            population      
##  Length:55          Min.   :   8.0   Length:55          Min.   :   56882  
##  Class :character   1st Qu.:  56.5   Class :character   1st Qu.: 1344331  
##  Mode  :character   Median : 123.0   Mode  :character   Median : 3751351  
##                     Mean   : 266.8                      Mean   : 5677621  
##                     3rd Qu.: 332.0                      3rd Qu.: 6515716  
##                     Max.   :2821.0                      Max.   :37253956  
##                     NA's   :4

For the numeric values, the number of stores ranges from 8 stores per state/province to 2821 stores per state/province. The population ranges from 56882 to 37253956. The other variables are of type character.

4. Use `ggplotly` for EDA

Answer the following questions:

Are the number of Starbucks proportional to the population of a state? (scatterplot)
Is the caloric distribution of Starbucks menu items different for drinks and food? (histogram)
What are the top 20 words in Starbucks menu items? (bar plot)

p1 <- ggplot(sb_locs_state, aes(x = population, y = n_stores,
                                colour = state)) +
  geom_point(alpha = 0.8) +
  theme_bw() 


ggplotly(p1)

4a) Answer:

The plot does appear to show a general trend, where as the population grows, the number of Starbucks stores also increases. However, the correlation here is not strictly linear, as some states (like the state with the abbreviation WA) have more stores than expected based on their population. But overall, yes, the number of Starbucks stores is correlated with population, but not perfectly proportional.

p2 <- ggplot(sb_nutr, aes(x = Calories, fill = Category)) +
  geom_histogram(alpha = 0.6) +
  theme_bw() 

ggplotly(p2)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

4b) Answer:

Yes, the caloric distribution of Starbucks menu items is different for drinks and food. The majority of drinks have a lower calorie count, clustering between 0 calories and 300 calories, whereas foods mainly cluster between 100 calories all the way to 600 calories. So, drinks tend to have a lower caloric range, while food items generally have higher calorie values.

p3 <- sb_nutr |> 
  unnest_tokens(word, Item, token = "words") |>
  count(word, sort = T) |>
  head(20) |>
  ggplot(aes(fct_reorder(word, n), n)) +
  geom_col() +
  coord_flip() +
  theme_bw()

ggplotly(p3)

4c) Answer:

The top word is iced, as it is seen 32 times in the sb_nutr dataset. tazo, bottled, sandwich, and chocolate are also in the top 20 words.

5. Scatterplots using `plot_ly()`

Create a scatterplot using plot_ly() representing the relationship between calories and carbs. Color the points by category (food or beverage). Is there a relationship, and do food or beverages tend to have more calories?

sb_nutr |>
  plot_ly(x = ~Calories, y = ~`Carb. (g)`, 
          type = "scatter", mode = "markers", color = ~Category)

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

5a) Answer:

Yes, there is a relationship between calories and carbs, as in the scatterplot, generally higher caolories lead to higher carbs. Food tends to have more calories than drinks.

Repeat this scatterplot but for the items that include the top 10 words. Color again by category, and add hoverinfo specifying the word in the item name. Add layout information to title the chart and the axes, and enable hovermode = "compare".
What are the top 10 words and is the plot much different than above?

topwords <- sb_nutr |> 
  unnest_tokens(word, Item, token = "words") |>
  group_by(word) |>
  summarise(word_frequency = n()) |>
  arrange(across(word_frequency, desc)) |>
  head(10)
  

sb_nutr |> 
  unnest_tokens(word, Item, token = "words") |>
  filter(word %in% topwords$word) |>
  plot_ly(
    x = ~Calories,
    y = ~`Carb. (g)`,
    type = "scatter", 
    mode = "markers",
    color = ~Category,
    hoverinfo  = "text",
    text = ~paste0("Item: ", word)
  ) |>
  layout(
    title = "Cal vs Carbs",
    xaxis = list(title = "Calories"),
    yaxis  = list(title = "Carbs"),
    hovermode = "compare"
    
  )

## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels

5b) Answer:

6. `plot_ly` Boxplots

Create a boxplot of all of the nutritional variables in groups by the 10 item words.
Which top word has the most calories? Which top word has the most protein?

sb_nutr_long <- sb_nutr |>
  unnest_tokens(word, Item, token = "words") |>
  filter(word %in% topwords$word) |>
  pivot_longer(cols = c(Calories, `Fat (g)`, `Carb. (g)`,
                        `Fiber (g)`, `Protein (g)`), names_to = "Nutrient", values_to = "value")


plot_ly(data = sb_nutr_long, 
        x = ~word,
        y = ~value, 
        color = ~Nutrient,
        type = "box" ) |>
  layout(
    title = "Nutrient values for the top 10 word items",
    xaxis = list(title = "Item word"),
    yaxis = list(title = "Nutritional Value")
  )

1. Answer:

The top word that has the most calories are sandwiches, as their median is the highest (460), and their tails are the highest - indicating that in general they have more calories compared to the other words. The words that have the highest protein are sandwich and egg. Both their medians are pretty similar (19 and 18.5 respectively). The box (and the tails) for the word sandwich are larger, indicating more variation compared to the protein amount in the word egg.

7. 3D Scatterplot

Create a 3D scatterplot between Calories, Carbs, and Protein for the items containing the top 10 words
Do you see any patterns (clusters or trends)?

sb_nutr_long <- sb_nutr |>
  unnest_tokens(word, Item, token = "words") |>
  filter(word %in% topwords$word[1:10]) |>
  plot_ly(
    x = ~Calories,
    y = ~`Carb. (g)`,
    z = ~`Protein (g)`,
    color = ~word,
    type = "scatter3d",
    mode = "markers",
    marker = list(size = 5)) |>
  
    layout(
      title = "3D scatterplot of Calories, Carbs, and Protein",
      scene = list(
        xaxis = list(title = "Calories"),
        yaxis = list(title = "Carbohydrates (g)"),
        zaxis = list(title = "Protein (g)")
      )
    )
  
sb_nutr_long

## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors

1. Answer:

There seems to be a very weak overall trend (as it is quite spread out). For the most part, I can see that higher calories generally lead to higher carbs and protein. So yes, there does seem to be an overall trend.

8. `plot_ly` Map

Create a map to visualize the number of stores per state, and another for the population by state. Add custom hover text. Use subplot to put the maps side by side.
Describe the differences if any.

# Set up mapping details
set_map_details <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('steelblue')
)

# Make sure both maps are on the same color scale
shadeLimit <- 125

# Create hover text
sb_locs_state$hover <- with(sb_locs_state, paste("Number of Starbucks: ", n_stores, '<br>', "State: ", state.y, '<br>', "Population: ", population))

# Create the map
map1 <- plot_geo(sb_locs_state, locationmode = "USA-states") |>
  add_trace(z = ~n_stores, text = ~hover, locations = ~state, color = ~n_stores, colors = "Purples") |>
  layout(
    title = "starbucks store by state", geo = set_map_details)
map1

## Warning: Ignoring 4 observations

map2 <- plot_geo(sb_locs_state, locationmode = "USA-states") |>
  add_trace(z = ~population, text = ~hover, locations = ~state, color = ~n_stores, colors = "Purples") |>
  layout(
    title = "starbucks store by state", geo = set_map_details)
map2

subplot(map1, map2)

## Warning: Ignoring 4 observations

1. Answer:

When put side by side, I do think that states with higher population tend to have a higher number of Starbucks stores. For instance, California has the highest population and also the highest number of Starbucks stores, Texas has the second highest population and the second largest number of stores. Overall, the maps look relatively similar (color-wise). Therefore, there isn’t much of a difference.

Bonus) Type in comment of submission: fav Starbucks drink

Lab 11 - Interactive Visualization

Learning Goals

Lab Description

Deliverables

Steps

0. Install and load libraries

1. Read in the data

2. Look at the data

3. Format and merge the data

4. Use `ggplotly` for EDA

5. Scatterplots using `plot_ly()`

6. `plot_ly` Boxplots

7. 3D Scatterplot

8. `plot_ly` Map

Lab 11 - Interactive Visualization

Learning Goals

Lab Description

Deliverables

Steps

0. Install and load libraries

1. Read in the data

2. Look at the data

3. Format and merge the data

4. Use ggplotly for EDA

5. Scatterplots using plot_ly()

6. plot_ly Boxplots

7. 3D Scatterplot

8. plot_ly Map

4. Use `ggplotly` for EDA

5. Scatterplots using `plot_ly()`

6. `plot_ly` Boxplots

8. `plot_ly` Map